Both deep learning and traditional methods have been used to conduct network-wide, multi-step traffic state prediction. For our attacks, we use two deep learning models and two traditional models as target models. While deep learning models provide state of the art performance, traditional models are easier to interpret and implement. Among traditional models, linear regression is conceptually simple and requires a few parameters to train and historical average is non-parameterized, which requires no training and uses less data and computation for prediction. These models have been widely used in traffic forecasting [1]}, [2]} and benchmarking [3]}, [4]}.
